Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
#load data
df = px.data.gapminder()
df.head()
Out[2]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [3]:
# It was told in the lecture that it is allowed to copy the same code for the first 3 exercises

df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
figure = px.bar(df_2007_new, x='pop', y=df_2007_new.index, text_auto='.2s', color=df_2007_new.index)
figure.update_layout(yaxis={'categoryorder': 'total ascending'})
figure.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [4]:
figure = px.bar(df_2007_new, x='pop', y=df_2007_new.index, text_auto='.2s', color=df_2007_new.index)
figure.update_layout(yaxis={'categoryorder': 'total ascending'})
figure.show()

Question 3:¶

Add text to each bar that represents the population

In [5]:
figure = px.bar(df_2007_new, x='pop', y=df_2007_new.index, text_auto='.2s', color=df_2007_new.index)
figure.update_traces(textposition="outside")
figure.update_layout(yaxis={'categoryorder': 'total ascending'})
figure.show()

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [6]:
df_all = df.groupby(['continent', 'year']).sum().reset_index()
figure = px.bar(df_all, x='pop', y='continent', color='continent', animation_frame='year', 
                animation_group='continent', hover_name='continent', range_x=[0,4000000000])
figure.update_layout(yaxis={'categoryorder': 'total ascending'})
figure.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [7]:
df_countries = df.groupby(['country', 'year']).sum().reset_index()
figure = px.bar(df_countries, x='pop', y='country', color='country', animation_frame='year', 
                animation_group='country', hover_name='country', range_x=[0,1400000000])
figure.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
figure.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [8]:
figure = px.bar(df_countries, x='pop', y='country', color='country', animation_frame='year', 
                animation_group='country', hover_name='country', range_x=[0,1400000000], height=1000)
figure.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
figure.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [9]:
figure = px.bar(df_countries, x='pop', y='country', color='country', animation_frame='year', 
                animation_group='country', hover_name='country', range_x=[0,1400000000], 
                range_y=[len(df_countries['country'].unique())-10.5, len(df_countries['country'].unique())-0.5])
figure.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
figure.show()
In [ ]: